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A novel adaptive dual swarm intelligence based image quality enhancement approach with the modified SegNet -RBM-based Alzheimer Segmentation and classification

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Abstract

In this research, a novel metaheuristic-based approach for enhancing the image quality of Magnetic resonance imaging (MRI) scans for Alzheimer’s disease (AD) classification and segmentation is proposed. The proposed approach involves four phases: data collection, preprocessing, segmentation, and classification. The dataset used in this study is collected from an open-source internet platform and contains four classes of AD. To improve the quality of the collected images, various enhancement techniques are employed, such as local entropy-weighted histogram equalization using an adaptive wind-driven optimization algorithm and guided filter-based image denoising using an adaptive Salp Swarm Optimization algorithm. In addition to this, geometric transformations, image augmentation, noise reduction, and grayscale conversion are also utilized. The proposed segmentation model employs a drop-block in Segmentation Network (SegNet) for regularized feature learning, resulting in highly efficient pixel-wise segmentation. A modified loss function is also introduced for fine-tuning the performance of SegNet. The classification layer of SegNet utilizes a Restricted boltzmann machine (RBM) model for classifying AD. Performance evaluation of the proposed approach is done by considering performance metrics such as accuracy, precision, recall, F1-measure, Root means square error (RMSE), and mean absolute error (MAE). The proposed approach outperforms existing techniques such as Convolutional neural network (CNN), Recurrent neural network (RNN), Long short-term memory (LSTM), Understanding networks (UNET), and SegNet, demonstrating the effectiveness of the proposed metaheuristic-based image quality enhancement approach. The implementation of the proposed approach is carried out using Matlab.

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Data availability

All the data is collected from the simulation reports of the software and tools used by the authors. Authors are working on implementing the same using real-world data with appropriate permissions.

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Authors and Affiliations

  1. Department of CSE, Koneru Lakshmaiah Education Foundation, AP, Vaddeswaram, India

    Raju Anitha

  2. Department of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana, 500043, India

    Durga Bhavani Dasari

  3. Indian Institute of Information Technology Design and Manufacturing (IIITDM), Kurnool, India

    P. Sandalya Sai Vivek

  4. Department of CSE, Sir C R Reddy College of Engineering, Eluru, AP, India

    Naga Madhavi Latha Kakarla

  5. School of Computing, Mohan Babu University, Tirupati, India

    M. Sirish Kumar

Authors
  1. Raju Anitha
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  2. Durga Bhavani Dasari
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  3. P. Sandalya Sai Vivek
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  4. Naga Madhavi Latha Kakarla
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  5. M. Sirish Kumar
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Correspondence to Raju Anitha.

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Anitha, R., Dasari, D.B., Vivek, P.S.S. et al. A novel adaptive dual swarm intelligence based image quality enhancement approach with the modified SegNet -RBM-based Alzheimer Segmentation and classification. Multimed Tools Appl 83, 29261–29288 (2024). https://doi.org/10.1007/s11042-023-16486-4

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  • DOI: https://doi.org/10.1007/s11042-023-16486-4

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